High Cancer Susceptibility Candidate 8 Expression Is Associated With Poor Prognosis of Pancreatic Adenocarcinoma: Validated Analysis Based on Four Cancer Databases

Wang, Yingyi; Yang, Yuemei; Wang, Yanfeng; Li, Xiaoou; Xiao, Yu; Wang, Wenze

doi:10.3389/fcell.2020.00392

Cited by 11 publications

(5 citation statements)

References 25 publications

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“…Among the seven lncRNAs used to construct the signature, AC092171.5 has previously been reported as an immune-and m6A-related lncRNA in pancreatic cancer and is significantly associated with patient prognosis (Wei et al, 2019;Cao et al, 2022). CASC8 has been suggested as a marker to predict the prognosis of PAAD and as a potential target for treatment (Wang et al, 2020;Ping et al, 2022), which is consistent with our findings. AC090114.2 was reported to be a pyroptosis-related lncRNA and was associated with PAAD prognosis and tumor immune microenvironment Frontiers in Genetics frontiersin.org (Zhao et al, 2022).…”

Section: Discussionsupporting

confidence: 91%

Construction of a cancer-associated fibroblasts-related long non-coding RNA signature to predict prognosis and immune landscape in pancreatic adenocarcinoma

Zhao

et al. 2022

Front. Genet.

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Background: Cancer-associated fibroblasts (CAFs) are an essential cell population in the pancreatic cancer tumor microenvironment and are extensively involved in drug resistance and immune evasion mechanisms. Long non-coding RNAs (lncRNAs) are involved in pancreatic cancer evolution and regulate the biological behavior mediated by CAFs. However, there is a lack of understanding of the prognostic signatures of CAFs-associated lncRNAs in pancreatic cancer patients.Methods: Transcriptomic and clinical data for pancreatic adenocarcinoma (PAAD) and the corresponding mutation data were obtained from The Cancer Genome Atlas database. lncRNAs associated with CAFs were obtained using co-expression analysis. lncRNAs were screened by Cox regression analysis using least absolute shrinkage and selection operator (LASSO) algorithm for constructing predictive signature. According to the prognostic model, PAAD patients were divided into high-risk and low-risk groups. Kaplan-Meier analysis was used for survival validation of the model in the training and validation groups. Clinicopathological parameter correlation analysis, univariate and multivariate Cox regression, time-dependent receiver operating characteristic (ROC) curves, and nomogram were performed to evaluate the model. The gene set variation analysis (GSVA) and gene ontology (GO) analyses were used to explore differences in the biological behavior of the risk groups. Furthermore, single-sample gene set enrichment analysis (ssGSEA), tumor mutation burden (TMB), ESTIMATE algorithm, and a series of immune correlation analyses were performed to investigate the relationship between predictive signature and the tumor immune microenvironment and screen for potential responders to immune checkpoint inhibitors. Finally, drug sensitivity analyses were used to explore potentially effective drugs in high- and low-risk groups.Results: The signature was constructed with seven CAFs-related lncRNAs (AP005233.2, AC090114.2, DCST1-AS1, AC092171.5, AC002401.4, AC025048.4, and CASC8) that independently predicted the prognosis of PAAD patients. Additionally, the high-risk group of the model had higher TMB levels than the low-risk group. Immune correlation analysis showed that most immune cells, including CD8+ T cells, were negatively correlated with the model risk scores. ssGSEA and ESTIMATE analyses further indicated that the low-risk group had a higher status of immune cell infiltration. Meanwhile, the mRNA of most immune checkpoint genes, including PD1 and CTLA4, were highly expressed in the low-risk group, suggesting that this population may be “hot immune tumors” and have a higher sensitivity to immune checkpoint inhibitors (ICIs). Finally, the predicted half-maximal inhibitory concentrations of some chemical and targeted drugs differ between high- and low-risk groups, providing a basis for treatment selection.Conclusion: Our findings provide promising insights into lncRNAs associated with CAFs in PAAD and provide a personalized tool for predicting patient prognosis and immune microenvironmental landscape.

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Section: Discussionsupporting

confidence: 91%

Construction of a cancer-associated fibroblasts-related long non-coding RNA signature to predict prognosis and immune landscape in pancreatic adenocarcinoma

Zhao

et al. 2022

Front. Genet.

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“…The competing endogenous RNA mechanism has been constructed using the TCGA-PAAD dataset by Wenjuan Zhu et al, who then identified CASC8 as a potential therapeutic target for pancreatic cancer 46 . This has also been confirmed by Yingyi Wang et al 47 . Afterward, the model was systematically validated for its reliable applicability and clinical predictive accuracy in terms of PCA, survival analysis of clinical subgroups and different cohorts, and independent prognostic analysis.…”

Section: Discussionsupporting

confidence: 77%

Cuproptosis-related lncRNA scoring system to predict the clinical outcome and immune landscape in pancreatic adenocarcinoma

Huang,

Gong,

et al. 2023

Sci Rep

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Cuproptosis is a recently discovered novel programmed cell death pathway that differs from traditional programmed cell death and has an important role in cancer and immune regulation. Long noncoding RNA (lncRNA) is considered new potential prognostic biomarkers in pancreatic adenocarcinoma (PAAD). However, the prognostic role and immune landscape of cuproptosis-related lncRNA in PAAD remain unclear. The transcriptome and clinical data of PAAD were obtained from The Cancer Genome Atlas (TCGA) database. Cuproptosis-related lncRNA was identified using Pearson correlation analysis. The optimal lncRNA was screened by Cox and the Least Absolute Shrinkage and Selection Operator (LASSO) regression mode, and for the construction of risk scoring system. PAAD patients were divided into high- and low-risk groups according to the risk score. Clinicopathological parameter correlation analysis, univariate and multivariate Cox regression, time-dependent receiver operating characteristic (ROC) curves, and nomogram were performed to evaluate the model. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to explore differences in biological function between different risk groups. Single-sample gene set enrichment analysis (ssGSEA) and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm were used to analyze the differences in tumor immune microenvironment (TIME) in different risk groups of PAAD. Additionally, the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was used to predict immunotherapy response and identify potential immune beneficiaries. Immune checkpoints and tumor mutation burden (TMB) were also systematically analyzed. Finally, drug sensitivity analysis was used to explore the reactivity of different drugs in high- and low-risk groups to provide a reference for the selection of precise therapeutic drugs. Six cuproptosis-related lncRNAs (AL117335.1, AC044849.1, AL358944.1, ZNF236-DT, Z97832.2, and CASC8) were used to construct risk model. Survival analysis showed that overall survival and progression-free survival in the low-risk group were better than those in the high-risk group, and it is suitable for PAAD patients with different clinical characteristics. Univariate and multifactorial Cox regression analysis showed that risk score was an independent prognostic factor in PAAD patients. ROC analysis showed that the AUC values of the risk score in 1 year, 3 years and 5 years were 0.707,0.762 and 0.880, respectively. Nomogram showed that the total points of PAAD patients at 1 year, 3 years, and 5 years were 0.914,0.648, and 0.543. GO and KEGG analyses indicated that the differential genes in the high- and low-risk groups were associated with tumor proliferation and metastasis and immune regulatory pathway. Immune correlation analysis showed that the amount of pro-inflammatory cells, including CD8+ T cells, was significantly higher in the low-risk group than in the high-risk group, and the expression of immune checkpoint genes, including PD-1 and CTLA-4, was increased in the low-risk group. TIDE analysis suggests that patients in the low-risk group may benefit from immunotherapy. Finally, there was significant variability in multiple chemotherapeutic and targeted drugs across the risk groups, which informs our clinical drug selection. Our cuproptosis-related lncRNA scoring system (CRLss) could predict the clinical outcome and immune landscape of PAAD patients, identify the potential beneficiaries of immunotherapy, and provide a reference for precise therapeutic drug selection.

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“…CASC8 is located on chromosome 8q24, and its specific upregulation in pancreatic adenocarcinoma might indicate poor prognosis 45 . These findings are consistent with our results.…”

Section: Discussionmentioning

confidence: 99%

Identification of m6A/m5C/m1A-associated LncRNAs for prognostic assessment and immunotherapy in pancreatic cancer

Huang

Zhang

et al. 2023

Sci Rep

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Methylation of RNA plays an important role in cancer. Classical forms of such modifications include N6-methyladenine (m6A), 5-methylcytosine (m5C), and N1-methyladenine (m1A). Methylation-regulated long non-coding (lnc) RNAs are involved in various biological processes, such as tumor proliferation, apoptosis, immune escape, invasion, and metastasis. Therefore, we performed an analysis of transcriptomic and clinical data of pancreatic cancer samples in The Cancer Genome Atlas (TCGA). Using the co-expression method, we summarized 44 m6A/m5C/m1A-related genes and obtained 218 methylation-associated lncRNAs. Next, with COX regression, we screened 39 lncRNAs that are strongly associated with prognosis and found that their expression differed significantly between normal tissues and pancreatic cancer samples (P < 0.001). We then used the least absolute shrinkage and selection operator (LASSO) to construct a risk model comprising seven lncRNAs. In validation set, the nomogram generated by combining clinical characteristics accurately predicted the survival probability of pancreatic cancer patients at 1, 2, and 3 years after diagnosis (AUC = 0.652, 0.686, and 0.740, respectively). Tumor microenvironment analysis showed that the high-risk group had significantly more resting memory CD4 T cells, M0 macrophages, and activated dendritic cells and fewer naïve B cells, plasma cells, and CD8 T cells than the low-risk group (both P < 0.05). Most immune-checkpoint genes were significantly different between the high- and low-risk groups (P < 0.05). The Tumor Immune Dysfunction and Exclusion score showed that high-risk patients benefited more from treatment with immune checkpoint inhibitors (P < 0.001). Overall survival was also lower in high-risk patients with more tumor mutations than in low-risk patients with fewer mutations (P < 0.001). Finally, we explored the sensitivity of the high- and low-risk groups to seven candidate drugs. Our findings indicated that m6A/m5C/m1A-associated lncRNAs are potentially useful biomarkers for the early diagnosis and estimating the prognosis of, and ascertaining the responses to immunotherapy in, patients with pancreatic cancer.

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High Cancer Susceptibility Candidate 8 Expression Is Associated With Poor Prognosis of Pancreatic Adenocarcinoma: Validated Analysis Based on Four Cancer Databases

Abstract: The aim of this study was to explore the association between the expression of a long non-coding RNA (lncRNA), cancer susceptibility candidate 8 (CASC8), and pancreatic adenocarcinoma (PAAD).

Cited by 11 publications

References 25 publications

Construction of a cancer-associated fibroblasts-related long non-coding RNA signature to predict prognosis and immune landscape in pancreatic adenocarcinoma

Construction of a cancer-associated fibroblasts-related long non-coding RNA signature to predict prognosis and immune landscape in pancreatic adenocarcinoma

Cuproptosis-related lncRNA scoring system to predict the clinical outcome and immune landscape in pancreatic adenocarcinoma

Identification of m6A/m5C/m1A-associated LncRNAs for prognostic assessment and immunotherapy in pancreatic cancer

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